System, Rfid Chip, Server and Method for Capturing Vehicle Data

2. The server device of claim 1, wherein the location information includes global positioning satellite coordinate information.

4. The server device of claim 1, wherein the controller is further configured to generate an alert message to be sent to the user when the controller determines that the user is within a certain proximity to one or more of the plurality of vehicles based upon the plurality of messages and the pick-up request.

5. The server device of claim 1, wherein the server device is a central assignment system combined with one or more databases.

6. The server device of claim 1, wherein the server device is further configured to assign a specific one of the plurality of vehicles to the user based upon the output value.

7. The server device of claim 6, wherein the server device is further configured to generate an assignment message to be sent to the user informing the user of the assigned specific one of the plurality of vehicles and an expected time at which the vehicle will arrive at the location information associated with the pick-up request.

8. The server device of claim 1, wherein the output value is indicative of one of the plurality of vehicles and wherein the controller is further configured to track the user and the vehicle from an initial time of the pick-up request until the user arrives at a destination indicated in the pick-up request based upon the messages and the pick-up request.

12. The server device of claim 1, wherein the controller is further configured to generate the output value from a trained model.

13. The server device of claim 12, wherein the trained model includes one of a trained Self-Organizing Map (SOM) and a trained neural network model (NNM).

15. The method of claim 14, further comprising inputting the determined one or more locations of interest and one or more historical locations related to the user into a trained model to generate the output value.

16. The method of claim 14, further comprising generating the one or more output values associated from a trained model.

17. The method of claim 16, wherein the trained model includes one of a trained Self-Organization Map (SOM) and a trained neural network model (NNM).